A systematic review of recurrent neural network adoption in missing data imputation

Missing data is a pervasive challenge in diverse datasets, often resulting from human error, system faults, and respondent non-response. Failing to address missing data can lead to inaccurate results during data analysis, as incomplete data sequences introduce biases and compromise the distribution...

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Bibliographic Details

Main Authors:	Nur Aqilah, Fadzil Akbar, Mohd Izham, Mohd Jaya, Mohd Faizal, Ab Razak, Nurul Aqilah, Zamri
Format:	Article
Language:	en en en
Published:	ResearchGate 2025
Subjects:	QA75 Electronic computers. Computer science T Technology (General)
Online Access:	https://umpir.ump.edu.my/id/eprint/42750/1/Acceptance%20Letter%20%281%29.pdf https://umpir.ump.edu.my/id/eprint/42750/2/A%20systematic%20review%20of%20recurrent%20neural%20network%20adoption%20in%20missing%20data%20imputation.pdf https://umpir.ump.edu.my/id/eprint/42750/13/A%20systematic%20review%20of%20recurrent%20neural%20network.pdf http://dx.doi.org/10.12785/ijcds/1571041166 https://umpir.ump.edu.my/id/eprint/42750/
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